docs: def _transform_col(self, x, i):
          """Encode one categorical column into sparse matrix with one-hot-encoding.
  
          Args:
              x (pandas.Series): a categorical column to encode
              i (int): column index
  
          Returns:
              X (scipy.sparse.coo_matrix): sparse matrix encoding a categorical
                                           variable into dummy variables
          """
  
          labels = self.label_encoder._transform_col(x, i)
          label_max = self.label_encoder.label_maxes[i]
  
          # build row and column index for non-zero values of a sparse matrix
          index = np.array(range(len(labels)))
          i = index[labels > 0]
          j = labels[labels > 0] - 1  # column index starts from 0
  
          if len(i) > 0:
              return sparse.coo_matrix((np.ones_like(i), (i, j)),
                                       shape=(x.shape[0], label_max))
          else:
              # if there is no non-zero value, return no matrix
              return None